Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods

Mustafa, Raza Ul; Nawaz, M. Saqib; Lali, M. Ikram Ullah; Zia, Tehseen; Mehmood, Waqar

doi:10.22452/mjcs.vol30no1.5

Cited by 42 publications

(19 citation statements)

References 21 publications

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“…Further, Twitter network features are exploited to extract informative links for users and to find the posting patterns of companies and their followers. Since the network size of Twitter is much smaller than Facebook [16], [17], we analyze our methodology with Twitter for simplicity and to lower the complexity.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Productivity of the Brands on Social Media With Respect to Their Responsiveness

et al. 2019

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Social online marketing is expanding fast with the evolution and recent development in the information and communication technology. Investigating how companies are exploiting social media for marketing, advertisement, and consumer's engagement is gaining more and more interest. In this paper, brands/companies data on Twitter is collected and analyzed to compute the overall company response on Twitter. Responsiveness of a company is inferred from three features: company popularity, average company replies, and average followers' replies. Twitter network features are used in calculating the posting frequency for companies and their followers. It is shown that the proposed approach can be used in finding the responsiveness of companies and their followers. Furthermore, useful links for a brand consumer is extracted and the posting behavior of brands and their followers is determined with the help of Twitter network features, such as retweet count and geolocation. This paper contributes to the literature on how Twitter data and its network structure features can be exploited in finding the responsiveness and posting behavior of companies and their followers. We believe that this approach can be used effectively in developing prediction and information-filtering systems, particularly the personalized-recommendation systems.

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Section: Introductionmentioning

confidence: 99%

Quantification of Productivity of the Brands on Social Media With Respect to Their Responsiveness

et al. 2019

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“…High precision is achieved for SVM and LR (91.9% and 92.7% respectively). On the other hand, high recall and fmeasure was achieved for NB (Mustafa et al, 2017b).…”

Section: Resultsmentioning

confidence: 99%

Human aggressiveness and reactions towards uncertain decisions

al.¹

2019

Int. j. adv. appl. sci.

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Big data is a term that defines data sets are so large or complex that traditional data processing applications are inadequate. Data comes with different formats such as Multilanguage, structured, unstructured and emails. Challenges dealing with a huge amount of data includes analysis, search, sharing, transfer, and visualization. Not all the data collected contains useful information, so there's a need to refine this data in order to filter out the useful information. Tweets posted on Twitter are expressed as opinions. These opinions can be used for different purposes such as to take public views on uncertain decisions. These decisions have a direct impact on the user's life such as violations and aggressiveness are common causes. For this purpose, we have collected opinions on some popular decision taken in the past decade from Twitter. We have divided the tweet text into two classes; Anger (negative) and positive. We have proposed a prediction model to predict public opinions towards such decisions. We used Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR) classifier for a text classification task. Furthermore, we have also compared SVM results with NB, LR. The research will help us to predict early behaviors and reactions of people before the big consequences of such decisions. Moreover, the results highlight the feasibility of using social media to predict public opinions.

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“…For their first model, the success rate is 76.4%, and for the second model, the rate is 70.9%. Mustafa et al (2017) used social network data and applied machine learning techniques to achieve around 75% accuracy in predicting the outcomes. The body of work discussed above has, in most cases, segregated one day internationals and T20 matches as it is indeed difficult to use the same model to predict these two formats.…”

Section: Literature Reviewmentioning

confidence: 99%

Managing In-play Run Chases in Limited Overs Cricket Using Optimized CUSUM Charts

Bose

Chakraborty

2019

JSA

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In this paper, we present the application of CUSUM control charts to analyze the outcome of a limited overs cricket match during the second innings. This approach makes use of the target set in the first innings and the runs scored from each ball in the second innings. We have analyzed the ball-by-ball data of 1180 One Day Internationals and 537 T20 Internationals played between 2006 and 2017. To improve the accuracy of our models, we have applied genetic algorithm to generate optimal control chart parameters that maximize the accuracy of the model outcome. Our analyses are found to be correct in 80-89% of ODI matches and 76-84% of T20 matches, depending on the stage of in-play analysis.

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Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods

Cited by 42 publications

References 21 publications

Quantification of Productivity of the Brands on Social Media With Respect to Their Responsiveness

Quantification of Productivity of the Brands on Social Media With Respect to Their Responsiveness

Human aggressiveness and reactions towards uncertain decisions

Managing In-play Run Chases in Limited Overs Cricket Using Optimized CUSUM Charts

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